Computational geometry: an introduction
Computational geometry: an introduction
The onion technique: indexing for linear optimization queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Efficient query evaluation on probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Probabilistic skylines on uncertain data
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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Uncertain data is inherently important in a lot of real-world applications, such as environmental surveillance and mobile tracking. Probabilistic convex hull is very useful for discovering the territory of imprecise data in such applications with a high confidence. In order to deal with this, we propose and study probabilistic convex hull queries based on the possible world semantics, which are able to retrieve the objects whose probability of being on the convex hull is at least α. The demonstration is based on animal tracking whose GPS coordinate is no longer considered to be precise due to device limitation or privacy issues. We demonstrate two interesting results from studying the migration habit of one specific species and the correlation between species through probabilistic convex hull queries.